In the medical arena, a plethora of computing environments and systems are engaged for the acquisition, storage, retrieval, presentation and distribution of a myriad of information relating to patients, procedures, administrative tasks, etc. An example of such a system in the imaging field might be a Picture Archiving and Communication Systems (PACS). PACS are a combination of computers and/or networks dedicated to the storage, retrieval, presentation and distribution of images. While images may be stored in a variety of formats, the most common format for image storage within the PACS system is Digital Imaging and Communications in Medicine (DICOM). DICOM is a standard in which radiographic images and associated meta-data are communicated to the PACS system from imaging modalities for interaction by end-user medical personnel.
Medical personnel spend a significant amount of their time addressing administrative tasks. Such tasks include, for example, documenting patient interaction and treatment plans, preparing billing, reviewing lab results, recording observations and preparing reports for health insurance. Time spent on performing such tasks diminish the time available for patients and in some instances lead to inaccurate and hastily compiled reports or records when personnel are faced with the need to see multiple patients.
In order to address time deficiency issues, the current trend in the medical field is to automate as many health care related processes as possible by leveraging various technologies, and thereby freeing up personnel to spend more time with patients rather than performing administrative tasks. Another objective in this arena is to ensure that administrative tasks are accomplished in an accurate and consistent manner. One approach to achieving this objective is to provide a standardized representation for healthcare related data, particularly within the various specialty areas, such as radiology, cardiology, etc.
Health care data is not easily reusable by disparate groups in the radiological field because it is stored with different methods and in different formats across a wide range of information technology. Various initiatives by groups and organizations across the globe, including the National Institutes of Health, Food and Drug Administration, and other medical bodies, have driven a set of standards for the consolidation of medical information into a common framework. One such standard is RadLex, which is a standard radiological lexicon proposed by the Radiological Society of North America, for uniform indexing and retrieval of radiology information. RadLex is a taxonomy having class hierarchies. RadLex functions essentially as a dictionary of terms and the relationships among the terms. RadLex has some crucial limitations. The most significant of these limitations being the inability to support radiological findings and the relationships between findings and characteristics of the findings. What is needed is an extension to RadLex—an extension that provides domain specific modeling, which can then be applied to or utilized by a wide variety of applications such as, report tools, treatment analysis programs, tools for classification and verification of radiological information, and systems for improving radiological work flow. Such an extension would utilize an ontology that is domain specific in order to process radiological information.
The problems described above and the proposed solutions are not unique to just the United States or any other country or locale. Hence, another problem that arises in the context of existing solutions is the inability to provide accurate and efficient localization of solutions without the duplication of the entire environment. This problem is more pronounced when dealing with solutions that employ a domain ontology.
Ontology is a data model for the modeling of concepts and the relationships between a set of concepts. Ontologies are utilized to illustrate the interaction between the set of concepts and corresponding relationships within a specific domain of interest. Thus, the concepts and the relationships between the concepts can be represented in readable text, wherein descriptions are provided to describe the concepts within a specific domain and the relationship axioms that constrain the interpretation of the domain specific concepts.
Numerous current products and research efforts offer tools that streamline data integration. These products include centralized database projects such as the Functional Magnetic Resonance Imaging Data Center and the Protein Data Bank, distributed data collaboration networks such as the Biomedical Informatics Research Network, commercial tools for data organization, and systems for aggregating healthcare information such as Oracle Healthcare Transaction Base. In addition, tools have been developed to automatically validate data integrated into a common framework. Validation calls for techniques such as declarative interfaces between the ontology and the data source and Bayesian reasoning to incorporate prior expert knowledge about the reliability of each source
Automated data integration and validation require fewer human resources, but necessitates that data have well-defined a priori structure and meaning. The most successful approaches make use of a standardized master ontology that provides a framework to organize input data, as well as a technology scheme for augmenting and updating the existing ontology. This paradigm has been successfully applied in various ontologies including Biodynamic Ontology, Gene Ontology, Mouse Gene Database, and the Mouse Gene projects, which provide a taxonomy of concepts and their attributes for annotating gene products. The Unified Medical Language System (UMLS) Metathesaurus and Semantic Network, combine multiple emerging standards to provide a standardized ontology of medical terms and their relationships
Ontology is a philosophy of what exists. In computer science, ontology is used to model entities of the real world and the relations between them to create common dictionaries for their discussion. Basic concepts of ontology include (1) classes of instances/things, and (2) relations between the classes, as described herein below. Ontology provides a vocabulary for talking about things that exist.
Relations, also referred to as properties, attributes and functions are specific associations of things with other things. Relations can include:
Relations between things that are part of each other, e.g., between a car and its tires;
Relations between things that are related through a process such as the process of creating the things, e.g., a painter and his/her painting; and
Relations between things and their measures, e.g., a tumorous mass and its size.
Some relations also associate things to fundamental concepts such as size, which would be related to large or small, or morphology which would be related to the shape of a mass such as round or linear.
Relations play a dual role in ontology. In one instance, individual things are referenced by way of properties, e.g., a person by a name or characteristic, or music by its title and composer. In another instance, knowledge being shared is often a property of things too. A thing can be specified by some of its properties, in order to query for the values of its other properties.
Not all relations are relevant to all things. It is convenient to discuss the domain of a relation as a “class” of things, also referred to as a category. Often domains of several relations may coincide.
There is flexibility in the granularity to which classes are defined. Assume automobile is a class. Ford cars may also be a class, with a restricted value of a brand property. However, this would only be a logical definition if Ford cars had attributes that were of interest or common to other automobiles. Generally, one can define classes as granular as an individual automobile unit, although an objective of ontology is to define classes that have important attributes.
There are a number of functionalities not provided by the systems described earlier. Accordingly, there is a need for a comprehensive system which is capable of enabling researchers to: 1) efficiently enter heterogeneous local data into the framework of the Unified Medical Language System (UMLS)—based ontology, 2) make necessary extensions to the standardized ontology to accommodate their local data, 3) validate the integrated data using expert rules and statistical models defined on data classes of the standardized ontology, 4) efficiently upgrade data that fails validation, and 5) leverage the integrated data for clinical outcome predictions. This is particularly the case in the field of radiology, and even more specifically within the various domains therein such as mammography.
To overcome some of the deficiencies earlier described, some existing systems have attempted to minimize the amount of effort that may be required to report on radiological findings. However, these systems suffer from a myriad of drawbacks. Essentially these solutions have: a non-standard library or vocabulary; no error, terminology or consistency checking; no collaboration or tool that can be used by other application programs, and issues relating to language, more specifically localization. It would be both cumbersome and unwieldy to duplicate systems for different localization environments or languages.
The shortcomings of the prior art are overcome and additional advantages are provided through the provision of a method for utilizing localized ontology that can consult other base ontology. The base ontology being based upon data obtained from unstructured and semi-structured knowledge sources to provide identification, validation and classification of localized radiological concepts.
The present invention addresses these needs as well as other needs.